CYBERPSYCHOLOGY & BEHAVIOR

Volume 10, Number 5, 2007© Mary Ann Liebert, Inc.DOI: 10.1089/cpb.2007.9959

Online versus Conventional Shopping: Consumers’ RiskPerception and Regulatory Focus

GUDA VAN NOORT, M.A.,1 PETER KERKHOF, Ph.D.,2 and BOB M. FENNIS, Ph.D.3

ABSTRACT

In two experiments, the impact of shopping context on consumers’ risk perceptions and reg-ulatory focus was examined. We predicted that individuals perceive an online (vs. conven-tional) shopping environment as more risky and that an online shopping environment, by itsrisky nature, primes a prevention focus. The findings in Study 1 demonstrate these effects byusing self-report measures for risk perception and prevention focus. In Study 2, we replicatedthese findings and demonstrated that the effect of an online shopping environment carriesover to behavior in a domain unrelated to shopping.

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INTRODUCTION

RISK PERCEPTION REGARDING the Internet is identi-fied as a primary obstacle to the future growth

of e-commerce and is one of the main predictors ofconsumers’ decisions to shop in an online or a con-ventional store.1 We propose that to more fully un-derstand online consumer behavior, investigatorsshould take the perceived risky nature of onlineshopping into account and focus on the prevalentmotivations and drives of consumers once they en-ter the online environment. We argue that becauseof the inherent risks of online shopping, avoidinglosses rather than achieving gains may become theconsumers’ prime goal. Higgins’s regulatory focustheory2 states that a different psychological systemoperates when the goal is loss-avoidance rather than

achieving gains. Promotion-focused self-regulation isconcerned with the absence or presence of positiveoutcomes, whereas prevention-focused self-regula-tion is concerned with the absence or presence of neg-ative outcomes and with safety. Accordingly, we ex-pect online (vs. conventional) shopping, by its riskynature, to induce a prevention-focused self-regula-tion among consumers. In Study 1, these hypothesesare tested using self-report measures of risk percep-tion and prevention focus. Since research has shownthat store reputation can affect risk perception,3 wealso manipulated store reputation to hold constantthe reputation of the store the participants had inmind. Study 2 aimed to replicate and further cor-roborate the findings of Study 1 by showing that theeffect of shopping environment carries over to be-havior in an unrelated domain.

1The Amsterdam School of Communication Research, University of Amsterdam, The Netherlands.2Department of Communication Science, VU University Amsterdam, The Netherlands.3Department of Marketing Communication and Consumer Psychology, University of Twente, The Netherlands.

Rapid Communication

STUDY 1

Method

Participants, design and procedure. Participantswere 91 students (44% male, mean age 19.94 (SD �2.41). The study involved a 2 (shopping environ-ment: conventional vs. online) � 2 (store reputation:reputable vs. nonreputable) between-subjects facto-rial design. The participants first completed scalesto measure perceived Internet and digital knowl-edge, prior online purchase behavior, and Internetusage. Next, they imagined a situation in which theywere shopping in either a reputable or nonreputableonline or conventional store. The participants thencompleted the risk perception and prevention focusscales.

Manipulation checks. As a manipulation check,participants rated whether the store they had inmind was an online store and whether it was rep-utable (3 items, � � 0.87).

Risk perception. Risk perception was assessedwith 9 items (� � 0.87), partially based on Jarven-paa and Todd.4 Participants were asked to estimatethe chance, on a 5-point scale (1 � small chance, 5 �great chance), that a certain risk (e.g., purchasedgoods and services will not meet the expectations)would emerge.

Self-reported prevention focus. Prevention-relateditems from a scale developed by Lockwood et al.5

were rephrased to indicate variations in situation-ally induced prevention focus. Example items in-cluded “When shopping in an (online or conven-tional) store, I am focused on negative outcomes.”The scale consisted of 8 items (� � 0.82), all ratedon a 5-point scale (1 � not at all true of me, 5 � verytrue of me).

Results

Manipulation checks and covariates. Sixteen partic-ipants did not have the correct shopping environ-ment in mind and were excluded from all analyses.Store reputation was successfully manipulated: par-ticipants in the reputable (vs. nonreputable) storecondition evaluated the reputation more favorably(Mreputable � 5.47, SD � 0.96 vs. Mnonreputable � 3.71,SD � 1.18; F(1,73) � 49.01, p � 0.001, �2 � 0.40).Only perceived Internet knowledge did correlatewith self-reported prevention focus (r(75) � �0.244,p � 0.04).

Risk perception. A 2 (online vs. conventional) � 2(reputable vs. nonreputable) ANOVA was con-ducted with the mean risk perception score as thedependent variable. As predicted, participants findonline shopping more risky than conventional shop-ping (Monline � 2.89, SD � 0.64 vs. Mconventional � 2.24,SD � 0.67; F(1,71) � 14.79, p � 0.001, �2 � 0.17).Furthermore, shopping in a nonreputable store wasperceived as more risky than shopping in a rep-utable store (Mnonreputable � 2.76, SD � 0.77 vs. Mrep-

utable � 2.36, SD � 0.59; F(1,71) � 6.26, p � 0.02,�2 � 0.08). No interaction effect was found.

Self-reported prevention focus. A 2 (online vs. con-ventional) � 2 (reputable vs. nonreputable) AN-COVA with the prevention focus scale as the de-pendent variable and Internet knowledge as acovariate showed only a main effect of shopping en-vironment. Participants in the online condition weremore prevention focused than in the conventionalcondition (Monline � 2.69, SD � 0.67 vs. Mconventional �2.36, SD � 0.74; F(1,70) � 5.05, p � 0.03, �2 � 0.07).Mediation by risk perception was significant ac-cording to the Sobel test: z � 2.18, p � 0.03.

Summary and discussion

The findings show that online (vs. conventional)shopping, by its risky nature, induces a preventionfocus. To sustain these findings in Study 2, we as-sessed prevention focus in an implicit and nonob-trusive way. Following Zhou and Pham,6 we expectthat the prevention focus primed by online shop-ping carries over to a seemingly unrelated task. Totest this hypothesis, we used a procedure similar tothe one used by Shah et al.7 They asked participantsto complete an anagram task in which either the po-tential gains (i.e., promotion) or the potential losses(i.e., prevention) of the anagram score were madesalient. Their findings show that a fit between indi-viduals’ regulatory focus and task instruction re-sults in a better performance. In a similar vein, weexpect that individuals in the online shopping en-vironment condition perform better on a preven-tion-framed (vs. promotion-framed) anagram task.

STUDY 2

Method

Participants and procedure. Seventy-seven stu-dents participated (19% male, mean age 22.8, SD �2.33). Four students did not complete the test andwere excluded from the analyses. The shopping en-

NOORT ET AL.732

vironment manipulation and the risk perceptionand self-reported prevention focus scales were sim-ilar to those in Study 1. Next, we tested whether theeffect spills over to a seemingly unrelated anagramtask.6 Participants were randomly assigned to theprevention or promotion task condition. The de-pendent measure was performance on solving 10anagrams, calculated by the number of correct an-swers (maximum 28).

Results

Risk perception and self-reported prevention focus.Again, the participants in the online condition per-ceived shopping as more risky (Monline � 2.90, SD �0.68 vs. Mconventional � 1.83, SD � 0.41; F(1,71) �65.37, p � 0.001, �2 � 0.48) and were more preven-tion focused according to the prevention focus scale(Monline � 2.83, SD � 0.65 vs. Mconventional � 2.28,SD � 0.66; F(1,71) � 12.94, p � 0.001, �2 � 0.16).The effect on self-reported prevention focus wasagain mediated by risk perception (z � 4.36, p �0.001).

Task performance. A 2 (online vs. conventional) �2(prevention- vs. promotion-framed anagram in-struction) ANOVA with the anagram score as thedependent variable yielded only the predicted in-teraction effect, F(1,69) � 4.13, p � 0.05, �2 � 0.06.Simple effects analysis revealed that the effect oftask instruction was significant in the online shop-ping condition, F(1,69) � 5.26, p � 0.03, �2 � 0.07(Mprevention � 19.50, SD � 2.55 vs. Mpromotion � 17.50,SD � 3.29) but not in the conventional shoppingcondition, F � 1. Risk perception did not mediatethis effect.

GENERAL DISCUSSION

The present findings indicate that consumers’risk perception is more than just a motive that un-derlies decisions to shop in an online or a conven-tional store. Both self-report and implicit measuresshow that by its risky nature, online shoppingevokes prevention-focused self-regulation. Interest-ingly, experienced online shoppers do not stop per-ceiving the Internet as a risky shopping channel andare as prevention focused as the inexperiencedshoppers.

Since prevention-focused (vs. promotion–fo-cused) self-regulation is known to affect consumer

information processing and choice,6 the findingshave implications for theories about online con-sumer behavior. For example, the kind of informa-tion that online consumers search for might be af-fected by their prevention focus. Whereas thenatural tendency of a marketer is to stress positiveproduct features, in an online store it may be wiseto stress the absence of negative features, since thisfits the predominant regulatory focus of the onlineconsumer. Future research should focus on whatkind of online information is persuasive given thatonline consumers may be relatively more preven-tion focused.

REFERENCES

1. Miyazaki AD, Fernandez A. Consumer perceptions ofprivacy and security risks for online shopping. TheJournal of Consumer Affairs 2001; 35:27–44.

2. Higgins ET. Beyond pleasure and pain. American Psy-chologist 1997; 52:1280–1300.

3. Chauduri A, Holbrook MB. The chain of effects frombrand trust and brand affect to brand performance: therole of brand loyalty. Journal of Marketing 2001;65:81–93.

4. Jarvenpaa SL, Todd PA. (1997) Is there a future for re-tailing on the Internet? In: Peterson RA, ed. Electronicmarketing and the consumer. Thousand Oaks, CA: Sage,pp. 139–54

5. Lockwood P, Jordan CH, Kunda Z. Motivation by pos-itive or negative role models: regulatory focus deter-mines who will best inspire us. Journal of Personalityand Social Psychology 2002; 83:854–64.

6. Zhou R, Pham MT. Promotion and prevention acrossmental accounts: when financial products dictate con-sumers’ investment goals. Journal of Consumer Re-search 2004; 31: 125–35.

7. Shah J, Higgins ET, Friedman RS. Performance incen-tives and means: how regulatory focus influences goalattainment. Journal of Personality and Social Psychol-ogy 1998; 74: 285–93.

Address reprint request to:G. van Noort

Amsterdam School of Communication Research (ASCOR)

Kloveniersburgwal 481012 CX Amsterdam

The Netherlands

E-mail: G.vanNoort@uva.nl

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